Trajectories of medication use and polypharmacy among children with cerebral palsy

BACKGROUND: Children with cerebral palsy (CP) may have chronic exposure to polypharmacy to address several medical needs, but there is little research on the topic to inform surveillance methods and clinical practice. OBJECTIVES: To identify the trajectories of medication number and pediatric polypharmacy (≥2 concurrent medications) exposure over 3.5 years among children with CP. METHODS: This cohort study used commercial claims from January 1, 2015, to December 31, 2018 (4-year period). Children with CP, aged 5-18 years by January 1, 2016, and with continuous health plan enrollment for all 4 years, were included and categorized as with or without co-occurring neurological/ RESULTS: Of the 1,252 children with CP, 600 were in the CP only cohort (mean [SD]; age, 11.4 [4.1] years; 46.0% female) and 652 were in the CP + NDDs cohort (age, 11.9 [4.1] years; 41.3% female; 32.7% had ≥2 of the NDDs). For the primary GBTM, 3 trajectory groups were identified for CP only: on average, no prescribed medications (69.7% of the cohort), 1 medication/month (24.8%), and 4 medications/month (5.5%). Five trajectory groups were identified for CP + NDDs: 0 (22.4%), 1 (25.6%), 2 (25.2%), 4 (18.4%), and 6 (8.4%) prescribed medications/month. For the secondary GBTM, 3 trajectory groups were identified for CP only: 80.5% were characterized as negligible probability of polypharmacy exposure, 10.8% as low probability, and 8.7% as high probability. Five trajectory groups were identified for CP + NDDs: 37.9% as negligible probability of polypharmacy exposure, 32.8% as constantly high probability, and 29.2% as changing probability (eg, increasing/decreasing). CONCLUSIONS: Children with CP are chronically exposed to differing levels of polypharmacy. Findings can help establish polypharmacy surveillance practices. Studies need to determine if polypharmaceutical strategies are balanced to optimize health and development for children with CP.


Plain language summary
Children with cerebral palsy (CP) have many health care needs that can be managed pharmaceutically. As a result, polypharmacy may be a concern. This study provides estimates of how many children with CP, aged 5-18 years, are exposed to varying levels of polypharmacy (≥2 concurrent medications) over 4 years. This study also provides preliminary evidence that 4-year exposure to a higher number of medications may be associated with a greater likelihood of exposure to potentially inappropriate medications and opioids.

Implications for managed care pharmacy
Clinicians should be aware of the chronic exposure to a higher number of medications when caring for their pediatric patients with CP. A higher number of medications was associated with a greater likelihood of exposure to potentially inappropriate medications. Particular attention may be needed for medication combinations that may interact to create or exacerbate health problems and lessen medication effectiveness.
Children with cerebral palsy (CP) have many health care needs, including clinical management of abnormal muscle tone and other conditions, such as sialorrhea, gastrointestinal complications, and pain. 1,2 Children with CP may also have co-occurring neurological and neurodevelopmental disabilities (NDDs) with the most common being epilepsy, intellectual disabilities, and autism spectrum disorders. [3][4][5] These NDDs carry their own set of medical conditions, which can increase the complexity of treating the symptomatology of the child with CP. As the constellation of medical comorbidities among children with CP can be managed in part by several medications, polypharmacy may be a concern, which in pediatrics refers to the concurrent use of at least 2 prescribed medications. 6 To date, the composition and appropriateness of (poly)pharmaceutical therapies for children with CP has received little research attention.
One major issue of polypharmacy is whether these therapies are optimized to produce (ensure) healthful aging and developmental progress for children with complex medical needs. There is currently a limited availability of evidence-based guidelines to develop safe and effective polypharmaceutical strategies, which is problematic as these strategies can carry risks and benefits. 7 Polypharmacy may increase the risk for harm due in part to adverse drug effects and drug-drug interactions, [8][9][10][11] which may also decrease the effectiveness of chronic pharmaceutical therapies. 12 A positive association between polypharmacy exposure and the likelihood of being prescribed potentially inappropriate medications has been reported. 13,14 In children, potentially inappropriate medications are defined as "medications or medication classes that should generally be avoided in persons younger than 18 years because they pose an unnecessarily high risk for children and a safer alternative is available." 15 Opioids can be used to manage pediatric pain symptoms despite therapeutic uncertainty, insufficiently established protocols, and alternative medications 16,17 and thus may be a potentially inappropriate medication in certain cases. Pain is a common symptom in children with CP and often comorbid with other conditions that can be pharmaceutically managed. 18,19 It has yet to be established if polypharmacy in children with CP is associated with an increased likelihood of being prescribed opioids and, indeed, if opioid prescription in these scenarios is appropriate.
However, chronic polypharmacy may be necessary to effectively manage complex medical profiles for children with CP, and short-term polypharmacy strategies may successfully quell acute health exacerbations common to children with CP, such as from pneumonia or postsurgical complications. [20][21][22] Thus, surveillance efforts are needed to develop our understanding of polypharmacy profiles for children with CP, which was recently identified as a key clinical research area by clinicians and caregivers of children with complex neurological conditions. 23 Investigations of polypharmacy often use cross-sectional designs. Although this provides useful information at the cohort level, it lacks the ability to track how individuals use medications over time. Group-based trajectory modeling (GBTM) is a longitudinal analytic technique that can be used to identify groups of individuals within a cohort who have similar baseline values and trajectories of the outcome under investigation. 24 In the context of polypharmacy, 25 GBTM can help to identify different patterns of medication use and differing levels of polypharmacy (eg, the number of concurrent medications) over time. Findings from such investigations may inform future clinical research into optimizing polypharmaceutical therapies.
The primary objective of this study was to identify medication prescription patterns among children with CP with and without NDDs over a 3.5-year duration. To enhance clinical interpretation, the secondary objective was to identify trajectories of polypharmacy exposure over the same period. 6 We then described the characteristics and composition of medication therapies for each trajectory group. Finally, we performed an exploratory analysis to examine if trajectory group membership was associated with a greater likelihood of being prescribed potentially inappropriate medications and opioids.

DESIGN AND DATABASE
This retrospective cohort study leveraged patient-level claims from January 1, 2015, to December 31, 2018, from Optum's deidentified Clinformatics Data Mart Database. This administrative claims database contains medical and outpatient pharmacy claims with representation across the United States. 26 For research, outpatient pharmacy prescriptions and medical conditions are identified by searching for unique codes attached to patient-level claims. As all data are deidentified prior to their administration CONCLUSIONS: Children with CP are chronically exposed to differing levels of polypharmacy. Findings can help establish polypharmacy surveillance practices. Studies need to determine if polypharmaceutical strategies are balanced to optimize health and development for children with CP.

CHARACTERISTICS
Sex, race, and US region of residence were sequestered from the baseline period. Age by January 1, 2016, was examined as continuous and age groups. As claims data do not provide information about pubertal status, which is complicated in boys and girls with CP, 27,28 the age categories are not sex specific but reflect general stages of development: 5-8 years, 9-13 years, and 14-18 years. A binary variable was constructed to indicate the presence of an assistive mobility device, including wheelchair, cane, walker, or gait trainer. The type of CP was described based on available International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification codes. All-cause health care resource use was assessed as the total count for any medical service and then by medical service subtype. 29

STATISTICAL ANALYSIS
We used GBTM for the CP only and CP + NDDs cohorts separately to identify distinct trajectories of medication exposure across monthly intervals for the 3.5-year observation period. The GBTM technique is a semiparametric application of finite-mixture modeling that identifies subgroups of individuals within a cohort with similar baseline and trajectory patterns of the outcome. 24 For the primary analysis, monthly medication exposure was defined as the number of unique medications prescribed and modeled using a zero-inflated Poisson model. For the secondary analysis, monthly medication exposure was defined using the pediatric polypharmacy definition of fewer than 2 vs at least 2 unique medications 6 using a logistic model. We modeled all trajectory groups using cubic polynomial structures 25 and followed recommendations for selecting the number of trajectory groups based on statistical, visual, interpretable, and parsimonious factors. 30,31 For example, one way to navigate model selection is by the change in Bayesian Information Criteria (BIC). BIC measures the model fit from the more complex model (eg, after adding a group) to the less complex model (eg, previous model). 32 A Bayes factor higher than 10-fold, which is calculated by exponentiating the difference in BICs, was considered strong evidence to retain the more complex model 30 (findings were the same using a different BIC approach 31 ).
We assessed the adequacy of selected models using published guidance. 30 All trajectory groups in all models had an average posterior probability greater than 0.94 (proposed threshold, >0.70) and had an odds of correct classification greater than 45.0 (proposed threshold, >5.0). Individual trajectories were depicted in spaghetti plots for each trajectory group for the primary GBTM model to show the extent of interindividual variation in patterns.
to researchers, the University of Michigan's institutional review board approved this study as nonregulated and patient consent was not required.

SAMPLE SELECTION
School-aged children with CP, aged 5-18 years by January 1, 2016 (index date), and with full continuous health plan enrollment from January 1, 2015, to December 31, 2018 (4-year period), were included. The year 2015 was considered the baseline period, and the following 3 years were considered the follow-up period. Children with CP were identified by at least 2 claims (any position), when each claim was on a separate day within 12 months of another, containing a pertinent International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification code for CP at any point during the 4-year period.
Children with CP were stratified as without NDDs (CP only) or with NDDs (CP + NDDs), which provides a reasonable proxy for medical complexity relevant to this study. The NDD group in this study included epilepsy, intellectual disabilities, and/or autism spectrum disorders, which were identified in the same manner as CP. A list of the codes used to identify the variables in this study is presented in Supplementary Table 1 (available in online article).

MEDICATIONS
This study included more than 300 chronic and "as needed" medications from outpatient pharmacy claims, which excluded "as needed" medications that were used for the common cold, flu, allergies, and immediate preoperative or postoperative use for nausea/diarrhea. First, all medications prescribed to participants during this sampling period were assessed using the generic naming convention. Then, the selection of medications of interest was identified by the team's pharmacist (author S.R.E.). The list of medications included is presented in the Supplementary Code List.
We identified the number and composition of unique medications that each child was exposed to each month from 6 months prior to the index date through the 3 years of follow-up. Specifically, exposure was determined based on the date of prescription and the number of days supplied for each medication, which allowed us to determine monthly exposure (ie, if the days supplied crossed into the monthly interval). We did not consider medication switching patterns within or outside the original medication class. This 3.5-year observation period was used for medications, since access to claims started on January 1, 2015, allowing for prescribed medication refills that have up to a 6-month supply per fill to be sufficiently detected. a priori. The Firth correction was applied to all logistic regression models, which is a penalized likelihood regression technique that can be used to minimize analytic bias due to few outcome events with small sample sizes. 33 Effect estimates are reported as odds ratios (ORs) with 95% CIs.
Analyses were performed using SAS version 9.4 (SAS Institute), and P values less than or equal to 0.05 (twotailed) were considered statistically significant.

Results
Of the 3,394 children aged 5-18 years identified as having CP, 1,121 were excluded, as they did not have continuous health plan enrollment during the baseline period, and another 1,021 were excluded, as they did not have continuous health plan enrollment for the 3-year follow-up period. The baseline descriptive characteristics for the final 1,252 Using the trajectory groups from the primary GBTM analysis, baseline characteristics and the most frequently prescribed medications (defined as prescribed to >10% per cohort) were described for each trajectory group. Because of the Data Use Agreement to preserve patient deidentification, variables with a sample size less than 11 were suppressed.

SENSITIVITY ANALYSIS
We performed the primary GBTM analysis using a cohort with continuous enrollment from January 1, 2015, to December 31, 2016 (a 2-year period), to determine if trajectory groups in the primary analysis were subject to selection bias by requiring 4 full years of continuous health plan enrollment.

EXPLORATORY ANALYSIS
This analysis explored whether the trajectory groups from the primary GBTM model were associated with a greater likelihood of being prescribed potentially inappropriate medications and opioids. Given the exploratory nature of this analysis, the trajectory groups from the primary analysis were not revised to omit potentially inappropriate medications and opioids. The list of potentially inappropriate medications was extracted from the inaugural version of the Key Potentially Inappropriate Drugs in Pediatrics List. 15 Of the 67 medications designated as "avoid" or use with "caution," 44 were excluded as they were out of the age range for this study (eg, a recommendation for infants), 12 were excluded as there was no prescription in this study's cohorts, and 1 was excluded as it is an antiseizure medication (lamotrigine). The remaining 10 medications were examined as a single binary indicator during the 3.5-year observation period and included at least 1 prescription for codeine, meperidine, olanzapine, desipramine, imipramine, chlorpromazine, haloperidol, promethazine, metoclopramide, or tramadol. Opioids were analyzed separately as a single binary indictor during the 3.5-year observation period and included at least 1 prescription for codeine, tramadol, oxycodone, hydrocodone, oxymorphone, morphine, or fentanyl. Information on the clinician specialty that prescribed the opioids is not available in this dataset. Identifying the reasons for the opioid prescription (eg, surgery) is beyond the scope of this exploratory analysis.
Logistic regression was used to examine the association between trajectory group membership and the 2 outcomes before and after adjusting for continuous age. Additional covariates for adjustment (eg, comorbidities) were not considered, given the small number of outcome events, possibly leading to biased parameter estimates. Age was considered the most important covariate for adjustment   For CP + NDDs, 5 trajectory groups were identified that remained relatively stable over the 3.5-year period (Figure 2; spaghetti plots shown in Supplementary  Figure 2): 22.4% were included in the 0 medications/ month group (consistent range, 0-1 medications/month); 25.6% were in the 1 medication/month group (consistent range, 0-3 medications/month); 25.2% were in the 2 medications/month group (consistent range, 1-4 medications/ month); 18.4% were in the 4 medications/month group (consistent range, 2-6 medications/month); and 8.4% were in the 6 medications/month group (consistent range, 5-8 medications/month).
The sensitivity analysis requiring continuous enrollment for a 2-year period found similar results for CP only (n = 946) (Supplementary Figure 3) and CP + NDDs (n = 1,023) (Supplementary Figure 4), suggesting that the primary analysis was not subject to substantial selection bias.

SECONDARY GBTM: TRAJECTORIES OF PEDIATRIC POLYPHARMACY
For the CP only cohort, 3 trajectory groups were identified across the 3.5-year period (Supplementary Figure 5). The largest group (80.5%) could be characterized as having a very low probability of polypharmacy exposure (average probability <0.05), whereas the other groups (10.8% and 8.7%) had a probability of exposure ranging from 0.12 to 0.45 and 0.75 to 0.98, respectively.
For the CP + NDDs cohort, 5 trajectory groups were identified (Supplementary Figure 6). The 2 largest groups could be characterized as having a consistently very low probability (group 1, 37.9%) or a consistently very high probability (group 5, 32.8%) of polypharmacy exposure. The other groups had either a declining probability (group 3, 8.7%) or an increasing probability of polypharmacy exposure, but with different timing and magnitudes (groups 2 and 4, 10.4% and 10.1%, respectively).

BASELINE CHARACTERISTICS AND MEDICATION COMPOSITION BY TRAJECTORY GROUP
The baseline descriptive characteristics for each trajectory group per cohort from the primary GBTM model are presented in Table 2. Notably, the average age was older with higher medication user groups for CP only, but this was less disparate for CP + NDDs. The higher medication user groups had a higher proportion of assistive mobility device users and at least 2 co-occurring NDDs (for CP + NDDs cohort) as well as higher baseline health care resource utilization.
Within both the CP only and CP + NDDs cohorts, trajectory groups 1 and 2 were combined. The medications prescribed to at least 10% of each trajectory group are children with CP, as CP only (n = 600) and CP + NDDs (n = 652), are presented in Table 1.

PRIMARY GBTM: TRAJECTORIES OF MEDICATION NUMBER
For CP only, 3 trajectory groups were identified that remained relatively stable over the 3.5-year period (Figure 1). Spaghetti plots shown in Supplementary Figure 1 indicate the "consistent" range (eg, disregarding temporary spikes) of medication number exposures per trajectory group, which was qualitatively determined. Of the cohort with CP only, 69.7% were included in the 0 medications/month group (consistent range, 0-1 medications/month); 24.8% were in the 1 medication/month group (consistent range, 0-3 medications/month); and 5.5% were in the 4 medications/month group (consistent range, 2-6 medications/month).

IMPLICATIONS FOR POLYPHARMACY SURVEILLANCE
This study provides estimates of the proportion of children with CP chronically exposed to varying levels of presented in Supplementary Table 2, which was generally consistent across years for each trajectory group.

EXPLORATORY ANALYSIS
Across the 3.5-year observation period, the proportion exposed to potentially inappropriate medications was 7.7% for CP only and 9.0% for CP + NDDs, and the proportion

CP + NDDs (n = 652)
The percent value represents the proportion of the cohort assigned to that trajectory group. CP = cerebral palsy; NDD = neurological/neurodevelopmental disability.

FIGURE 2
Trajectories of Monthly Unique Medication Number Exposure Over 3.5 Years for Children With CP With NDDs   TABLE 2 biological inter-activeness of the polypharmaceutical strategies used in children with CP create a net benefit or harm? To begin compiling preliminary information, the exploratory analysis provides evidence that children with CP, especially with co-occurring NDDs, exposed to a higher number of medications were more likely to be prescribed potentially inappropriate medications and opioids, although most associations were not statistically significant. It is important to note that the opioids codeine and tramadol were included in the list of potentially inappropriate medications. There was a large portion of children with CP exposed to opioids in this study: ~1 in 3. Therefore, the proportion exposed to potentially inappropriate medications may be driven in part by codeine and tramadol. Further, it is possible that potentially inappropriate medications and opioids were in part the reason for the higher number of medications in the trajectory group analysis. This may explain the association between those exposed to a higher number of medications and the greater likelihood of a prescription for potentially inappropriate medications and opioids. However, it is important to note that the trajectory groups reflect a longitudinal exposure pattern over a 3.5-year period. The medications included in the potentially inappropriate medication list and opioids are unlikely to be prescribed for such long periods of time. For example, opioids are used to manage pain but are less effective for managing chronic pain 35 and therefore may be an inappropriate choice of therapy for the ~1 in 3 children with CP who experience chronic pain. 36 Given the exploratory nature of this analysis, evidence to suggest the polypharmacy from a large, heterogeneous sample. Thus, it contributes to the early establishment of polypharmacy-based surveillance practices. However, this study accessed private insurance data, and findings may not be representative of children who are uninsured or have public insurance (eg, Medicaid). Based on population-based estimates derived from a nationally representative database among US children aged 0-17 years, 65.3% of children with CP were covered by private insurance in 2016. 34 When children with CP were compared based on private vs public-only insurance coverage, there were no differences in age, sex, or severity of CP, where 56.0%-60.6% were parent-reported to have "moderate or severe" CP. However, the CP group with private insurance had a lower proportion of Black children (6.8% vs 35.8%). Therefore, findings from this study should be interpreted within the context of this privately insured cohort.

IMPLICATIONS FOR CLINICAL PRACTICE
The finding that polypharmacy was prevalent in children with CP was expected. The medications prescribed to more than 10% of each trajectory group largely fall into the expected categories of inhaled medications, hypertonia management, seizure medications, and gastrointestinal symptom management.
However, it would be remiss to assume that the medications prescribed to children in the higher utilization groups were medically necessary and appropriate for use in combination with other prescribed medications. Thus, a question remains: does the volume, composition, chronicity, and  TABLE 3 can contain errors in coding health care billing procedures, and such data can lack information about the severity of CP. However, the stratification by co-occurring NDDs provides the necessary information relevant to the scope of this study. There was a limited ability to identify reasons why opioids were prescribed and for which patients. Therefore, findings should be interpreted as exploratory, and additional studies on the topic are needed. This study included a 4-year period ending in 2018, and it is unknown if the estimates in this study fully reflect prescription patterns today.

Conclusions
A considerable portion of children with CP with and without NDDs were exposed to polypharmacy over a recent 3.5-year period. This study also provides preliminary evidence that exposure to a higher number of medications may be associated with a greater likelihood of exposure to potentially inappropriate medications and opioids, but this warrants further investigation. Clinicians must balance optimizing overall health using simple or complex pharmacological therapies against possible adverse risks. More research is needed to assist in identifying the balance between medically necessary polypharmacy and avoiding potential harms.

LIMITATIONS
Limitations that may influence interpretations of this study must be discussed. Polypharmacy is an important quantitative measure, but it does not provide information about the quality of complex medication regimes, such as dosing schedules and the route of administration. [37][38][39] This study quantified exposure based on the date of supply and number of days supplied per medication, but it did not have access to information as to whether the child took the medication and adhered to the medication guidelines. Further, the monthly exposure was based on the number of unique medications per month, and switching medications was not considered in this study. It is therefore possible that some months for some children may have 2 sequential medications counted to address the same health condition, such as switching antiseizure medications because of complications. However, this is anticipated to be uncommon and have a negligible effect on the primary conclusions drawn. This study had access to outpatient medications only, which limited our ability to fully detect potentially inappropriate medications, as many of these medications can be prescribed in the inpatient setting. 10 The algorithm to identify CP and NDDs used all 4 years rather than the baseline year only. An assumption was made that the NDDs were developed prior to study entry, but some children may have developed epilepsy or an intellectual disability after study entry. We anticipate this to be rare and unlikely to bias the study findings. Administrative data reason for the potentially inappropriate medication or opioid prescription was not assessed (eg, surgery), nor was whether these prescriptions differed across other important impairments associated with CP, such as verbal communication. Further, the specialty of the clinician prescribing these medications was not available in this dataset. Studies are needed to identify longitudinal exposure to potentially inappropriate medications and opioids. Many of the most frequently prescribed medications in this study have similar side effect profiles as opioids. Increased risk of sedation with seizure, pain, and hypertonicity medications in this pediatric population should be considered by prescribers. Future studies could be designed to determine whether these prescriptions were problematic. Further, the Key Potentially Inappropriate Drugs in Pediatrics List of potentially inappropriate medications was derived from a rigorous process by a team of experts, 15 but the list is not specific to CP.

IMPLICATIONS FOR RESEARCH METHODOLOGIES
When examining medication exposure as the number per month in this study, the group-level trajectories remained relatively stable across time (ie, using GBTM), but the individuallevel variability was greater in the higher medication user groups (ie, using spaghetti plots). When examining medication exposure based on the pediatric polypharmacy definition, 6 there was notable variability in some of the trajectory groups, and some groups changed over time. Thus, care must be taken for interpretations of cross-sectional measures of polypharmacy because a measure taken from a single point in time may not reflect the child's long-term exposure. Further, care must be taken when evaluating